Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products.
They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right.
This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.
[maxbutton id=”4″ url=”https://doi.org/10.1162/dint_a_00033″ text=”Read more” linktitle=”Data Intelligence: FAIR Computational Workflows” ]Citation
Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober (2022):
FAIR Computational Workflows.
Data Intelligence 2(1-2):0
https://doi.org/10.1162/dint_a_00033
